Jasmine Adams
December 12, 2022
Your weather app doesn’t always match what you see when you look out the window. Despite considerable advances in weather reporting, the National Weather Service (NWS) — the primary data source for all weather service providers in the United States — struggles to maintain comparable accuracy and precision during periods of high volatility. Satellites, radars, and other forecasting tools synthesize complex weather patterns across large geographic areas and are not well suited for observing sudden changes in localized surface-level precipitation. In fact, radar – the NWS’s primary tool for measuring precipitation observed on the ground – does not measure surface-level rainfall at all. Instead, meteorologists infer how much it’s raining by emitting electromagnetic energy and analyzing the “echo” that precipitation particles reflect back. To improve the accuracy of its estimates, the NWS relies on a network of citizen volunteers who report daily precipitation and real-time changes in severe weather conditions.
United States National Radar
While reporting radar-predicted rainfall as rainfall “observed” at
the surface isn’t exactly a Watergate-level scandal (especially when
this information is on the NWS website), whether these more generalized
estimates reflect localized weather patterns during periods of increased
volatility has incredible implications for cities such as Washington,
DC, where urban topography, impervious surfaces, and rising temperatures
induced by climate change have led to accelerated rainfall volatility
and interior flooding (Cone, 2012; DC Silver Jackets, 2017; Zahura &
Goodall, 2022). This study thus aims to assesses the extent to which
hourly precipitation indicated by radar for the greater DC area
represents observed weather conditions in DC neighborhoods by comparing
the former to hourly rainfall measured by my personal weather station
(PWS) in Dupont Circle (Figure 1).
Figure 1 - Ambient Weather WS-7078 Smart Weather Station
| Sensory Array |
|---|
| 1 Antenna |
| 2 Rain collector |
| 3 UV / light sensor |
| 4 Mounting pole |
| 5 Mounting Base |
| 6 Balance indicator |
| 7 Wind cups |
| 8 Radiation shield |
| 9 Wind vane |
Weather Underground is a weather service provider that reports live
and historical weather data from a worldwide public network of personal
weather stations. To keep track of my station’s reported weather
conditions, I registered it on wundergound.com on November 13
and began tracking outdoor weather conditions the following day.
Official
weather data for the Washington, DC metropolitan area are estimated
via radar at the Washington/Baltimore regional weather station located
at Reagan Nation Airport. Data are reported hourly and are not available
beyond three days through any public source. Due to these reporting
constraints, I had to grab data from their website at least once every
three days to avoid any critical gaps in information. Though I managed
to gather all necessary data from the NWS website, the PWS at home in
Dupont Circle did not capture hourly rainfall data on November 27 due to
human error (i.e., my roommate mistakenly moving the weather station
just under our awning while I was away per my admittedly ambiguous
instructions). To remedy this shortcoming and mitigate other potential
measurement errors of which I am not aware, I cross reference my own
data with data from two other nearby personal weather stations in the
Weather Underground network located in Adams Morgan and just east of
Dupont Circle, respectively.
Unsurprisingly, this glorified science fair experiment came with some challenges and limitations. As alluded to before, radars and rain gauges measure rainfall through methods that are not apples to apples. While my home station and the Adams Morgan station have a rain gauge accuracy of ±7% and ±10% respectively, accuracy for the station east of Dupont and the radar at Reagan Nation Airport are unknown. Moreover, with limited information and quality control checks on other personal weather stations set ups, its plausible that those stations are vulnerable to other third party factors that may undermine the accuracy of their reports. Table 1 summarizes the data and design limitations of each data collection tool.
| Weather Station | NWS | Home | PWS1 | PWS2 |
|---|---|---|---|---|
| Station Location | Regean Airport | N of Dupont | E of Dupont | Adams Morgan |
| Station Type | Unknown | WS-7078 | Unknown | WS-1400-IP |
| Method of Rain Detection | Radar | Rain Gauge | Rain Gauge | Rain Gauge |
| Position of Detected Rainfall | Closer to Clouds | Surface Level | Surface Level | Surface Level |
| Measurement Accuracy | Unknown | ± 7% | Unknown | ± 10% |
| Risk of Roommate/Squirrel Interference | Low | High | Medium | Medium |
I copied data tables from the NWS and Weather Underground websites
from November 14 - December 10 and placed them into excel for
preliminary data cleaning. I then uploaded the data into R where I
filtered for hours in which at least one station recorded 0.01 inches of
rain or more. After gathering descriptive statistics for hours in which
it rained (Table 2), I compared recorded precipitation amounts between
each station using a series of OLS linear regressions. I also ran
regressions to assess whether variations in recorded temperature,
pressure, and humidity were comperable to the variation in measured
precipitation.
Although variation in measured rainfall between each station may be minimal, there is a statistically significant difference in rainfall reported by the NWS station and each of the other local stations.
Differences in measured rainfall between the three neighboring stations are smaller than between these stations and the NWS station.
Variation between my home station and the NWS station is greater for reported rainfall than for any other metric.
There have been approximately seven rainy days since data collection began on November 14. Across those 7 days, there were 45 hours for which at least 0.01 inches of rain was recorded (6 of which occurred on November 27 when my PWS was not positioned to capture any rain). Although this sample size is large enough to conduct a statistically significant analysis, it is still quite small. Table 2 displays the daily precipitation measured by each station.
| Date | NWS (DCA) | Home (Dupont) | East Dupont | Adams Morgan | Avg. Accum. |
|---|---|---|---|---|---|
| Nov 30 | 0.32 | 0.37 | 0.40 | 0.35 | 0.360 |
| Nov 27 | 0.24 | NA | 0.23 | 0.20 | 0.222 |
| Nov 25 | 0.15 | 0.17 | 0.17 | 0.19 | 0.170 |
| Nov 15 | 1.22 | 2.56 | 0.85 | 1.39 | 1.502 |
| Dec 7 | 0.05 | 0.01 | 0.02 | 0.02 | 0.025 |
| Dec 6 | 0.17 | 0.15 | 0.21 | 0.19 | 0.179 |
| Dec 3 | 0.31 | 0.31 | 0.37 | 0.32 | 0.327 |
As illustrated in Figure 2, aside from data collected on November 15,
the neighborhood weather stations recorded a similar amount of rainfall.
However, whether I keep or disregard outliers, particularly the two
highest values recorded by my PWS, has notable implications for the
estimated difference in hourly rainfall between stations.
A quick boxplot reveals that there are five outliers ranging from approx. 0.22 - 0.8 that I would be statistically justified in removing. However, due to the small sample size, I elect to remove only the two data points above 0.6 inches.
(Home Weather Station)
Similar to Figure 2, the line graph in Figure 4 illustrates
recorded hourly rainfall excluding outlying values of 0.69 or above.
| NWS Average | Home Average | p-value |
|---|---|---|
| 0.057 | 0.092 | 0.253 |
| NWS Average | EDup Average | p-value |
|---|---|---|
| 0.055 | 0.05 | 0.707 |
| NWS Average | AdMo Average | p-value |
|---|---|---|
| 0.055 | 0.059 | 0.769 |
| NWS | NWS | NWS | Home | Home | Adams Morgan | |
|---|---|---|---|---|---|---|
| Home | 0.06 *** | |||||
| (0.01) | ||||||
| E. Dupont | 0.06 *** | 0.14 *** | 0.07 *** | |||
| (0.00) | (0.02) | (0.01) | ||||
| Adams Morgan | 0.06 *** | 0.16 *** | ||||
| (0.00) | (0.01) | |||||
| N | 39 | 45 | 45 | 39 | 39 | 45 |
| R2 | 0.76 | 0.79 | 0.93 | 0.66 | 0.85 | 0.82 |
| All continuous predictors are mean-centered and scaled by 1 standard deviation. The outcome variable is in its original units. *** p < 0.001; ** p < 0.01; * p < 0.05. | ||||||
Finally, when comparing all weather measurements, it appears that the
temperature, dew point, pressure, and humidity reported by both stations
is highly comparable, with data measured by one station accounting for
91% - 100% of the variation in data collected by the other. Given that
rainfall measured by the NWS station accounts for only 76% of the
rainfall measured by the Home station, the final hypothesis is
correct.
| NWS Rain | NWS Temp | NWS Dew | NWS Pressure | NWS Humidiity | |
|---|---|---|---|---|---|
| Home Rain | 0.06 *** | ||||
| (0.01) | |||||
| Home Temp | 5.31 *** | ||||
| (0.18) | |||||
| Home Dew | 4.88 *** | ||||
| (0.23) | |||||
| Home Pressure | 0.14 *** | ||||
| (0.00) | |||||
| Home Humidity | 7.71 *** | ||||
| (0.41) | |||||
| N | 39 | 39 | 39 | 39 | 39 |
| R2 | 0.76 | 0.96 | 0.92 | 1.00 | 0.91 |
| All continuous predictors are mean-centered and scaled by 1 standard deviation. The outcome variable is in its original units. *** p < 0.001; ** p < 0.01; * p < 0.05. | |||||
Results suggest that measured rainfall is not distinctly different between the NWS station at Reagan National Airport and personal weather stations in the Dupont Circle and Adams Morgan area. Granted, this study had many limitations. The weather stations’ sample sizes were quite small and two of them had unknown measurement errors. The study also took place during the Fall when temperatures are cooler and rainfall is generally less volatile. I recommend that future studies use year round data, or at least data collected during warmer periods, as that could reveal a different picture about how well NWS weather station reports reflect localized weather conditions experienced on the surface.